Interpreting Microarray Expression DataUsing Text Annotating the Genes

Michael Molla, Peter Andreae, Jeremy Glasner, Frederick Blattner, Jude Shavlik

University of Wisconsin – Madison

The Basic Task

Given

Microarray Expression Data &

Text Annotations of Genes

Generate

Model of Expression

Motivation

• Lots of Data Available on the Internet– Microarray Expression Data– Text Annotations of Genes

• Maybe we can Make the Scientist’s Job Easier– Generate a Model of Expression Automatically– Easier First Step for the Human

Microarray Expression Data

• Each spot represents a gene in E. coli

• Colors Indicate Up- or Down-Regulation Under Antibiotic Shock

• Four our Purpose 3 Classes– Up-Regulated– Down-Regulated– No-Change

Microarray Expression Data

From “Genome-Wide Expression in Escheria Coli K-12”, Blattner et al., 1999

Our Microarray Experiment

• 4290 genes

• 574 up-regulated

• 333 down-regulated

• 2747 un-regulated

• 636 non enough signal

Text Annotations of Genes

• The text from a sample SwissProt entry (b1382)– The “description” field

HYPOTHETICAL 6.8 KDA PROTEIN IN LDHA-FEAR INTERGENIC REGION

– The “keyword” fieldHYPOTHETICAL PROTEIN

Sample Rules From a Model for Up-Regulation

• IF– The annotation contains FLAGELLAR AND

does NOT contain HYPOTHETICAL

OR– The annotation contains BIOSYNTHESIS

• THEN– The gene is up-regulated

Why use Machine Learning?

• Concerned with machines learning from available data

• Informed by text data, the leaner can make first-pass model for the scientist

Desired Properties of a Model

• Accurate– Measure with cross validation

• Comprehensible– Measure with model size

• Stable to Small Changes in the Data– Measure with random subsampling

Approaches

• Naïve Bayes– Statistical method– Uses all of the words (present or absent)

• PFOIL– Covering algorithm– Chooses words to use one at a time

Naïve BayesFor each word wi, there are two likelihood ratios (lr):

lr (wi present) = p(wi present | up) / p(wi present | down)

lr (wi absent) = p(wi absent | up) / p(wi absent | down)

For each annotation, the lrs are combined to form a lr for a gene:

where X is either present or absent.

PFOIL

• Learn rules from data

• Produces multiple if-then rules from data

• Builds rules by adding one word at a time

• Easy to interpret models

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P FOIL

Naive Bayes

Accuracy/Comprehensibility Tradeoff

Stabilized PFOIL

• Repeatedly run PFOIL on randomly sampled subsets

• For each word, count the number of models it appears in

• Restrict PFOIL to only those words that appear in a minimum of m models

• Rerun PFOIL with only those words

Stability MeasureAfter running the algorithm N times to generate N rule sets:

Where:U = the set of words appearing in any rule set

count(wi) = number of rule sets containing word wi

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Stabilized P FOIL Error Rate

Stabilized P FOIL Stability

Unstabilized P FOIL Stability

Accuracy/Stability Tradeoff

Discussion

• Not very severe tradeoffs in Accuracy– vs. stability– vs. comprehensibility

• PFOIL not as good at characterizing data– suggests not many dependencies– need for “softer” rules

Future Directions

• M of N rules

• Permutation Test

• More Sources of Text Data

Take-Home Message

• This is just a first step toward an aid for understanding expression data

• Make expression models based on text in stead of DNA sequence.

Acknowledgements

• This research was funded by the following grants:NLM 1 R01 LM07050-01,

NSF IRI-9502990,

NIH 2 P30 CA14520-29, and

NIH 5 T32 GM08349.